Squeezy builds a local knowledge database of your code, so the first model call doesn't start from a blank repository.
how it works
Prepare locally, then ask the model with focus.
A coding agent gets expensive when it keeps rediscovering your code and carrying raw output forward. Squeezy understands the repository on your machine, reuses what is stable, right-sizes each turn, and shows you where the tokens went.
where cost leaks
Most of an agent's bill is not the work you asked for.
On a normal turn, only a slice of the spend is the change you wanted. The rest piles up from repeated discovery, raw command output, and old history carried turn after turn.
work the model had to do cost that piled up around it
the loop
Local preparation first. Focused model work second.
Broad questions narrow to specific files, symbols, diffs, and command output, not whole directories.
Repeated output becomes receipts, noisy output is shaped, and long sessions are compacted before they get expensive.
The model gets the useful context, and Squeezy tracks tokens, cache, tool output, and estimated spend.
the four layers
Where the saving comes from.
Each layer targets a different source of waste. They are independent, so they stack on a single turn instead of relying on one trick.
01
understand first
Understand the code first
Before spending a token, Squeezy reads your repository locally and works out which files and lines matter.
Squeezy finds the exact declarations, callers, and slices on your machine, then sends the model those instead of entire files.
A local code knowledge database spans 22 languages: every symbol, definition, caller, and reference. So "who calls this?" is one query, not a dozen file reads.
Your machine
01
Your question
“who calls this function?” 02
Local code understanding
22 languages · understood on your machine 03
The exact evidence
the right files and lines, nothing extra Provider
04
The model
a small, focused prompt 02
don't pay twice
Don't pay for the same bytes twice
Most of a coding session repeats: the same instructions, the same files, the same command output. Squeezy keeps that out of the bill.
Where the provider supports caching, Squeezy keeps stable instructions and context cache-friendly, so repeated turns cost less.
When the same file or output would be sent twice, Squeezy sends a short receipt that points back to the first copy, not the bytes again.
The model sees a compact tool index first and loads a full definition only when it's needed, which also keeps the cached prefix intact.
After an edit lands, Squeezy trims the now-stale parts of earlier reads in place, so later turns don't re-buy lines the change already replaced.
03
right-size turns
Right-size every turn
Not every turn deserves the biggest model or the longest history. Squeezy matches the effort and the context to the task in front of it.
Simple, mechanical turns start on a small, fast model and step up to the main one only when the task turns out to be hard.
As a session grows, older turns fold into a short summary of goal, progress, and decisions, so turn 30 doesn't pay for turns 1 through 29.
Build, test, and search output is trimmed to the part the model can act on, with the full log one fetch away.
Short-lived subagents explore or review in their own context and hand back a summary, instead of bloating the main thread.
04
stay in control
Control the bill, don't just watch it
Seeing where the tokens go only helps if you can act on it. Squeezy turns the numbers into control: cap the spend, force a turn cheaper or stronger, and see exactly where it all went.
Set an optional session cost cap or per-round token ceiling and Squeezy stops before it crosses the line, warning as you approach instead of after.
Send the next turn to the small model when you know it's easy, or to the main one when it isn't, without changing your defaults.
Cost and context views break down input, output, cache, tool output, and reasoning, with dollar estimates where the provider exposes them, so you know what to tune.
measured
40% less spend for the same result.
Across our 15-language code-understanding benchmark, Squeezy answered the same questions as an external baseline for 60 cents on the dollar at the median. Same model, same pricing, same grader. It cost less in every language, on both tiers tested, without giving up accuracy.